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Keynote Lectures

Engineering Software for Life in Cyber-Physical-Social Spaces
Bashar Nuseibeh, The Open University, United Kingdom

Empirical Approach to Learning from Data (Streams)
Plamen Angelov, Lancaster University, United Kingdom

Software Defined Cities
Salvatore Distefano, Università degli Studi di Messina, Italy

 

Engineering Software for Life in Cyber-Physical-Social Spaces

Bashar Nuseibeh
The Open University
United Kingdom
 

Brief Bio
Bashar Nuseibeh is Professor of Computing at The Open University (Director of Research 2001-2008) and a Professor of Software Engineering at Lero - The Irish Software Research Centre (Chief Scientist 2009-2012 and 2017-current). He is also a Visiting Professor at University College London (UCL) and the National Institute of Informatics (NII), Tokyo, Japan. Previously, he was a Reader (Associate Professor) in Computing at Imperial College London, Head of its Software Engineering Laboratory, and a Visiting Professor (2000-2015). He served as Editor-in-Chief of IEEE Transactions on Software Engineering and of the Automated Software Engineering Journal, and currently serves as Editor-in-Chied of ACM Transactions on Autonomous and Adaptive Systems. He chaired the ICSE Steering Committee and IFIP Working Group 2.9 (Requirements Engineering). He received an ICSE Most Influential Paper Award, a Philip Leverhulme Prize, an ASE Fellowship, and a Royal Academy of Engineering Senior Research Fellowship. He received an IFIP Outstanding Service Award (2009) and an ACM SIGSOFT Distinguished Service Award (2015). His research team received the 2017 IET Innovation Award n Cyber Security His research interests lie at the intersection of software engineering engineering, adaptive systems, and security & privacy. He holds a Royal Society-Wolfson Merit Award and two European Research Council (ERC) awards, including an ERC Advanced Grant on ‘Adaptive Security and Privacy’.


Abstract
People increasingly live in highly intertwined digital, physical, and social spaces, moving seamlessly between them, and experiencing life in a hybrid of such spaces. The opportunities and threats in these hybrid worlds are many, often new, and sometimes unexpected. They are the subject of much study by computer scientists, physical scientists, and social scientists, each community attempting to explain and influence the world in which they specialise. In this talk, I argue that software engineering has a critical role to play beyond just the development of the digital computations that constitute digital spaces. As software increasingly operates without and beyond the traditional boundaries of unitary machines (i.e. computers), and mediates the interactions that connect people (e.g., through social media), then software’s engineering is an activity that must also grow beyond its discipline boundaries, to become the very tool by which society expresses how it should be constructed and managed. This is not simply a philosophical position - my talk will present examples of software engineering of human activity systems that are adaptively secure, privacy aware, and forensically ready, to illustrate a changing role of software engineering in society.



 

 

Empirical Approach to Learning from Data (Streams)

Plamen Angelov
Lancaster University
United Kingdom
 

Brief Bio
Dr Plamen Angelov, is a Reader in Computational Intelligence and coordinator of the Intelligent Systems Research at Infolab21, Lancaster University, UK. He is a Senior Member of the IEEE and Chair of two Technical Committees (TC); TC on Standards, Computational Intelligence Society and TC on Evolving Intelligent Systems, Systems, Man and Cybernetics Society. He is also a member of the UK Autonomous Systems National TC, of the Autonomous Systems Study Group, NorthWest Science Council, UK and of the Autonomous Systems Network of the Society of British Aerospace Companies. He is a very active academic and researcher who authored or co-authored over 150 peer reviewed publications in leading journals (50+) peer-reviewed conference proceedings, a patent, a research monograph, a number of edited books, and has an active research portfolio in the area of computational intelligence and autonomous system modelling, identification, and machine learning. He has internationally recognised pioneering results into on-line and evolving methodologies and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems and autonomous machine learning. Dr. Angelov is also a very active researcher leading projects funded by EPSRC, ASHRAE-USA, EC FP6 and 7, The Royal Society, Nuffield Foundation, DTI/DBIS, MoD, industry (BAE Systems, 4S Information Systems, Sagem/SAFRAN, United Aircraft Corporation and Concern Avionica, NLR, etc.). His research contributes to the competitiveness of the industry, defence and quality of life through projects, such as the ASTRAEA project - a GBP 32M (phase I and GBP 30M phase II) programme, in which Dr. Angelov led projects on Collision Avoidance (GBP 150K, 2006/08), and Adaptive Routeing (GBP 75K; 2006/08). The work on this project was recognised by 'The Engineer Innovation and Technology 2008 Award in two categories: i) Aerospace and Defence and ii) The Special Award. Other examples of research that has direct impact on the competitiveness of UK industry and quality of life are the BAE Systems-funded project on Sense and Avoid (principal investigator, GBP 66K; 2006/07), BAE funded project on UAS Passive Sense, Detect and Avoid Algorithm Development (GBP 24K consultancy, a part of ASTRAEA-II, 2009), the BAE Systems-funded project (co-investigator, GBP 44K, 2008) on UAV Safety Support, EC-funded project (EUR 1.3M, co-investigator) on Safety (and maintenance) improvement trough automated flight data Analysis, the Ministry of Defence funded projects ('Multi-source Intelligence: STAKE: Real-time Spatio-Temporal Analysis and Knowledge Extraction through Evolving Clustering', GBP 30K, principal investigator, 2011 and Assisted Carriage: Intelligent Leader-follower algorithms for ground platforms, GBP 42K, 2009 which developed unmanned ground-based vehicle prototype taken further by Boeing-UK in a demonstrator programme in 2009-11), so called 'innovation vouchers by the North-West Development Agency-UK and Autonomous Vehicles International Ltd. (GBP 10K, 2010, principal investigator), MBDA-led project on Algorithms for automatic feature extraction and object classification from aerial images (GBP 56K, 2010) funded by the French and British defence ministries. Dr. Angelov is also the founding Editor-in-Chief of the Springer's journal on Evolving Systems and serves as an Associate Editor of several other international journals. He also Chairs annual conferences organised by IEEE, acts as Visiting Professor (2005, Brazil; 2007, Germany; 2010, Spain) regularly gives invited and plenary talks at leading companies (Ford, The Dow Chemical, USA; QinetiQ, BAE Systems, Thales, etc.) and universities (Michigan, USA; Delft, the Netherlands; Leuven, Belgium, Linz, Austria, Campinas, Brazil, Wolfenbuettel, Germany, etc). More information can be found at www.lancs.ac.uk/staff/angelov.


Abstract
The staggering proliferation of heterogeneous, large scale data sets and streams is recognised as an untapped resource which offers new opportunities for extracting aggregated information to inform decision-making in policy and commerce. However, currently existing methods and techniques for data mining involve a lot of prior assumptions, handcrafting and a range of other bottleneck issues: i) scalability – vast amounts of data which require high throughput automated methods (e.g. manual labelling of data samples can be prohibitive); ii) complex, heterogeneous data (including signals, images, text that may be uncertain and unstructured); iii) dynamically evolving, non-stationary data patterns, and the shortcomings of the “standard” assumptions about data distributions; iv) the need to hand craft features, parameters or set thresholds. As a result, a large proportion of the available data remains untapped. The key challenge now is to manage, process and gain value and understanding from the vast quantity of heterogeneous data without handcrafting and prior assumptions, at an industrial scale. In this talk a newly emerging theoretical framework which we call Empirical Data Analytics will be introduced and described and its relation to the probability, density, centrality, etc.



 

 

Software Defined Cities

Salvatore Distefano
Università degli Studi di Messina
Italy
 

Brief Bio
Salvatore Distefano is an Associate Professor at University of Messina, Italy, and Fellow Professor at Kazan Federal University, head of the Social and Urban Computing Group and of the Cisco Innovation Center in Kazan. He was formerly an Assistant Professor at Politecnico di Milano (2011-2015). In 2001 he got the master degree in Computer Engineering from University of Catania, and then, in 2006, he received the PhD degree on Computer Science and Engineering from University of Messina.
He authored and co-authored more than 170 scientific papers and contributions to international journals, conferences and books.
He took part to several national and international projects, such as Reservoir, Vision (EU FP7), SMSCOM (EU FP7 ERC Advanced Grant), Beacon, IoT-Open.EU (EU H2020).
He is a member of international conference committees and he is in the editorial boards of IEEE Transactions on Dependable and Secure Computing, International Journal of Performability Engineering, Journal of Cloud Computing, International Journal of Engineering and Industries, International Journal of Big Data, International Journal of Computer Science & Information Technology Applications, International Journal of Distributed Sensor Networks.
He has also acted as guest editors for special issues of the Journal of Risk and Reliability, Journal of Performability Engineering, ACM Performance Evaluation Review and IEEE Transactions on Dependable and Secure Computing
His main research interests include non-Markovian modelling; performance and reliability evaluation; dependability; Quality of Service/Experience; Service Level Agreement; Parallel and Distributed Computing, Grid, Cloud, Autonomic, Volunteer, Crowd, Edge, Fog Computing; Internet of Things; Smart Cities; Swarm and collective intelligence; Big Data; Software and Service Engineering. During his research activity, he contributed to the development of several tools such as WebSPN, ArgoPerformance, GS3 and Stack4Things.
He is also one of the co-founder of the SmartMe.io start up, a spin-off of the University of Messina established in 2017.


Abstract
A Smart City represents an improvement of today cities that strategically exploits many smart factors to increase the city sustainable growth and strengthen city functions, while ensuring citizen quality of life and health.
Cities can be perceived as ecosystems of "things" which citizens daily interact with: street furniture, public buildings, transportation, monuments, public lighting as well as personal smartphones.
Thanks to recent advances in ICT such things can be considered always interconnected also providing sensing and actuating facilities according to  Internet of Things and Cyber Physical Systems models.
Creating smart services that exploit such a complex infrastructure is a fundamental and current challenge, as well as making the Smart City ecosystem sustainable.
To this extent, a solution could be on adopting the Software Defined Cities (SDC) approach, which is based on an IoT-Cloud  infrastructure that, starting from the well known concept of Software Defined paradigms, is able to transform this complex ecosystem into a simple and "programmable" environment where municipalities, companies, scientists, and citizens can easily collaborate in developing innovative smart services.
This way, by enabling reuse and sharing of resources and services through programmability, a new wave of sustainable Smart Cities could be triggered by the  SDC, built up  from existing infrastructure and facilities thus reducing budgets and saving costs.
In this talk the main concepts and ideas behind the SDC approach will be discussed.
The adoption of the SDC approach on a real case study, the #SmartME (http://smartme.unime.it/) project, a low-cost crowdfunding initiative aiming at turning Messina smart, will be provided to show its feasibility and effectiveness.



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